Measuring and Modeling Attention
Measuring and Modeling Attention
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测量和建模注意力
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Andrew Caplin
中科院分区：
文献类型：
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作者：
Andrew Caplin
本文以Block&Marschak(1960)的观察为出发点，对注意选择的经济学研究进行了选择性的回顾。由于标准选择数据将效用和感知混为一谈，他们指出，对于注意力是内生的研究来说，这是不够的。综述的重点是他们的论点，即我们对注意力的理解的进步需要对新的基于选择的数据集和相应的测量方法进行建模。作为例子，详细介绍了最近基于测量和建模状态相关随机选择数据的注意力研究。下一步与战略注意力和学习动力相关的研究步骤被概述。如果Block&Marschak的论点是正确的，那么随着注意力研究的进步，设计新数据集将成为一项越来越重要的专业活动。
This article presents a selective review of economic research on attentional choice, taking an observation of Block & Marschak (1960) as its starting point. Because standard choice data conflate utilities and perception, they point out that it is inadequate for research in which attention is endogenous. The review focuses on their thesis that advances in our understanding of attention require modeling of novel choice-based data sets, and corresponding methods of measurement. By way of example, recent attentional research based on measuring and modeling state-dependent stochastic choice data is detailed. Next research steps in relation to strategic attention and the dynamics of learning are outlined. If the thesis of Block & Marschak is valid, engineering of new data sets will become an increasingly essential professional activity as attentional research advances.
影响因子：
3.6
作者：
Chabris CF;Laibson D;Morris CL;Schuldt JP;Taubinsky D
通讯作者：
Taubinsky D